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REVIEW 5 major objections 5 minor 53 references

Secure & Personalized Music-to-Video Generation via CHARCHA

T0 review · 5 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read The paper claims a fully automated pipeline that turns a single music audio file into a personalized music video, with visuals following lyrics, emotion, and rhythm, and a user's face inserted via LoRA trained on images collected by a…

desk verdict A likeable prototype with a genuinely new dual-use liveness/consent idea, but the 'secure' claim is unsupported as written and needs to be scoped down before this should be accepted anywhere. read the letter →

arxiv 2502.02610 v1 pith:IFGUAWUT submitted 2025-02-03 cs.AI cs.CVcs.HCcs.MM

Mehul Agarwal , Gauri Agarwal , Santiago Benoit , Andrew Lippman , Jean Oh This is my paper · ORCID
classification cs.AIcs.CVcs.HCcs.MM
keywords musicvideogenerationpersonalizationlow-rankadaptationLoRAfaceverificationCAPTCHAlivenessdetectionmultimodal
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims to build a fully automated pipeline, MVP, that takes only a music audio file and produces a music video whose visuals track the lyrics, emotional arc, and rhythmic pulse of the song. A listener can be inserted into the video as a recurring character by training a LoRA adapter on seven webcam images of their face. To collect those images ethically, the paper introduces CHARCHA, a CAPTCHA-style liveness protocol that has a live user perform six randomized facial expressions and head poses, verified with MediaPipe, before their likeness can be used. If the pipeline works as described, an ordinary listener can turn any song into a personal music video with no manual editing and some protection against unauthorized use of their face.

What carries the argument

The load-bearing components are the CHARCHA protocol—a CAPTCHA-style live-user verification that records seven webcam images while a person performs six randomized facial expressions and head poses verified in real time by MediaPipe—and the LoRA fine-tuning step that turns those seven images into a reusable character adapter for Stable Diffusion. Around these sit a chain of pretrained translators: Whisper (lyrics with timestamps), a DEAM-trained arousal/valence network over openSMILE features (emotion quadrants), GPT-4o (lyric/emotion to image prompts), Stable Diffusion 1.5 with style checkpoints (frames), and onset-strength-weighted spherical interpolation (rhythm-synced transitions).

What would settle it

A direct adversarial test in which participants attempt to pass CHARCHA using a printed photo, a phone displaying a video, or a real-time face-swap tool such as DeepFaceLive; if any of these succeeds often enough to beat the 6/10 threshold, the security claim collapses. A second check would be to recover the experimental determination of the 6/10 threshold, since the paper states it was determined experimentally but provides no data.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that music-to-video generation can be made both context-aware and identity-aware in one zero-shot pipeline: Whisper extracts timestamped lyrics, a music emotion recognition model maps arousal and valence into four affect quadrants, GPT-4o turns lyric/emotion pairs into cinematic image prompts, and Stable Diffusion 1.5 renders frames that are stitched by spherical interpolation whose weights are modulated by the music's onset strength envelope. Personalization is achieved by fine-tuning a LoRA adapter on seven CHARCHA-collected webcam images, which the authors report reproduces the participant's face in 81% of frames (92% of face-containing frames). CHARCHA itself is the paper's security contribution: it pairs a calibration step with six randomized, MediaPipe-verified actions and a pass threshold of 6/10, intended to prove a live, consenting human is on camera while simultaneously gathering the images needed for the LoRA.

Load-bearing premise

The entire security claim rests on the assumption that a determined impostor cannot pass CHARCHA by holding a photo, playing a recording, or running a live deepfake in front of the webcam—the paper itself concedes it is not yet hardened against DeepFaceLive, and the 6/10 threshold is said to be determined experimentally without reporting the experiment.

Editorial extensions

If this is right

  • If the pipeline is correct, users can generate a complete music video from a single MP3 file, without any video editing skill, and see their own face rendered in multiple artistic styles.
  • The CHARCHA protocol, if it holds, turns the identity-collection step into a liveness check, so the images used for personalization double as evidence that the person consented to being captured.
  • Combining lyric timestamps, emotion quadrants, and onset strength provides a recipe for synchronizing generated frames to both semantic content and musical beats, which could generalize beyond music videos to other audio-driven animation tasks.
  • Because the LoRA adapter is trained on only seven images, the approach suggests a low-data path to personalized character consistency in diffusion-based video generation.
  • The reported face-verification numbers (81% of frames, 92% of face frames) imply that a small set of expressive webcam shots is enough to make a generated character recognizable, if the measurement is trusted.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • CHARCHA's design could be lifted out of music videos and applied to any generative service that uses a person's likeness—social avatars, advertising, or synthetic media—making it a potential template for consent-gated identity use.
  • The threshold of 6/10 and the specific set of six actions are presented as experimentally determined, but the paper does not report the experimental data; an independent replication of that threshold experiment would tell whether the liveness bar is actually calibrated against real spoofing attempts.
  • Because the pipeline relies on a commercial LLM for prompt generation, the visual narrative quality may shift with model updates, which means the system's consistency over time is not guaranteed by the paper's architecture alone.
  • The CLIP similarity metric used to argue that generated videos do not merely copy training images is acknowledged by the authors as sensitive and misaligned with human judgment, so a human-evaluation study would be a stronger test of personalization fidelity.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper presents MVP, a fully automated pipeline that takes a music audio file as the only input and produces a music video whose visuals are intended to follow the lyrics, emotion, and rhythm of the song. The pipeline combines Whisper for lyrics, a DEAM-trained valence/arousal model, ChatGPT 4o for prompt generation, Stable Diffusion 1.5 with checkpoint models for stylization, and spherical interpolation with onset-strength scheduling for synchronization. Personalization is achieved by training a DreamBooth/LoRA adapter on webcam-captured images of the user, and these images are collected through CHARCHA, a CAPTCHA-style protocol that asks the user to perform a set of facial actions while MediaPipe verifies them in real time. The paper evaluates the pipeline qualitatively with video stills and reports face-verification metrics (81% / 11.1% / 92%) for identity reproducibility, plus an appendix CLIP similarity analysis. The authors claim that CHARCHA provides secure facial identity verification that protects against unauthorized use of a person's face, and the title and abstract describe the framework as 'secure.'

Significance. If the security and personalization claims were established, this would be a useful demonstration of an end-to-end music-to-video system with a plausible consent-capture mechanism. The pipeline is concretely described and integrates several open-source components (Whisper, openSMILE, DEAM, Stable Diffusion, MediaPipe, DeepFace), and the qualitative results show that style transfer and lyric-driven prompting are feasible with current models. The CHARCHA idea of combining liveness verification with data collection for personalization is novel and worth exploring. However, the central 'secure' claim is currently unsupported: CHARCHA as described verifies only that a face is live and performing actions, not that the person shown is the authorized individual, and the authors' own limitations section concedes vulnerability to DeepFaceLive. The identity metrics are also circular because they compare generated frames to the very images used to train the LoRA, and they lack error bars or a reported baseline. With a reframed security scope and strengthened evaluation, the contribution could be a solid creative-AI systems paper; in its present form the headline claims outrun the evidence.

major comments (5)
  1. [Abstract, §3.2, §5] The 'secure' claim in the title and abstract is not supported by the CHARCHA protocol as described. Section 3.2 verifies liveness by having the user perform actions in front of a webcam, but there is no binding to a pre-registered reference face, no trusted identity document, and no mechanism to establish that the person acting is the person whose likeness is being authorized. Because the action set is small and fixed (Figure 4 shows seven actions; the text says four and later nine), an attacker can pre-record a victim performing each action and replay the clips, and MediaPipe would score the landmarks as valid. A real-time face-swap tool such as DeepFaceLive would be even more direct, and the Limitations section (Section 5) concedes that CHARCHA needs strengthening 'against deep fake technologies like Deep Face Live.' The paper must either remove 'secure' and 'identity verification' from the claims and scope CHARCHA as a liveness/consent-capture protocol, or add an identity-binding mechanism and demonstrate its security against replay and face-swap attacks.
  2. [§4.2] The pass threshold of 6/10 is asserted to be 'determined experimentally as shown in the results,' but no experimental threshold analysis is reported. Section 4.2 describes three phases, including a break-it attempt in Phase 3, but the only quantitative result shown is the survey in Figure 5; there are no false-accept rates, false-reject rates, or any accuracy numbers for the MediaPipe action verification. Without this data, the claimed separation between legitimate users and impostors is not established. The authors should report the threshold-tuning experiment with error rates and, ideally, an ROC curve or confusion matrix.
  3. [Table 1, Figure 9, §A.3] The identity-reproducibility evaluation is partly circular. Table 1 compares generated video frames to the CHARCHA source images that were used to train the LoRA, so high similarity is expected by construction and does not measure generalization to unseen poses, expressions, or settings. The appendix's different-character baseline is described only qualitatively ('notably higher than would be expected') without reporting the numerical baseline scores or per-participant spread. Furthermore, the 81% / 11.1% / 92% figures have no error bars or per-subject variance for the seven participants. The evaluation should use held-out images not used in LoRA training, report baseline comparisons with variance, and state the number of frames and participants per metric.
  4. [§3.2, §4.2] The action set size is inconsistent across the paper: Section 3.2 says 'facial expressions and head poses (4)' and 'set of actions (4)', Figure 4 is captioned '7 CHARCHA Protocol Actions', and Section 4.2 Phase 1 refers to 'the list of nine actions.' This makes the protocol irreproducible. The authors must specify the exact action set, the total number of actions, and how the six actions in a test are sampled from that set.
  5. [§4.1, §A.2] The central claim that generated visuals are 'contextually aligned and synchronized with the music' is supported only by qualitative stills and a link to an external drive folder. No quantitative measure of lyric-to-visual alignment, emotion alignment, or beat synchronization is provided, and there is no user study of music-video quality. At a minimum, the authors should report an onset-alignment error metric (e.g., against the PLP beats described in Section A.5) or clearly scope the claim as a qualitative demonstration.
minor comments (5)
  1. [§4.2] Typo: 'emperically demonstrated' should be 'empirically demonstrated.'
  2. [§3.2] The protocol description says the test takes 60-90 seconds, but the stated timings (2-second calibration plus six actions of 10 seconds each with 5-second gaps) sum to about 92 seconds before any retry; please reconcile these numbers.
  3. [§3.2] The text says 'repeated for 6 actions' and later mentions 'seven screenshots'; it is unclear whether the calibration image is one of the seven or whether the action images alone are seven. Please clarify the exact number of images used for LoRA training.
  4. [§4.2, Figure 5] Figure 5 is described as a survey with n=16 participants, but the survey results are not summarized in the text; the reader cannot tell what the participants' comfort levels or pass rates were. Please add a brief description of the survey findings.
  5. [§A.3] The CLIP similarity analysis would be more convincing if the 'different character' baseline were plotted on the same axes in Figure 9, rather than described only in the text.

Circularity Check

1 steps flagged · score 4.0 of 10

The music-to-video core is an assembly of pretrained components and is not circular, but the personalization evaluation compares generated frames to the same CHARCHA images used to train the LoRA, so the identity-preservation result is partly self-referential.

  1. fitted input called prediction [Section 4.2 (Table 1) and Appendix A.3 (Figure 9), building on Section 3.2 LoRA training]
    "Our process involved training a Dreambooth LoRA model [36, 37] using CHARCHA user images. We then generated videos with the Stable Diffusion 1.5 Realistic Vision model [3] using the same prompts but different character LoRAs. To analyze reproducibility, we focused on 7 participants, comparing video frames to original CHARCHA images."

    The LoRA adapter is trained on the CHARCHA images, and those same CHARCHA images are then used as the reference ground truth for measuring whether the generated video frames reproduce the participant's face. High face-verification and CLIP similarity between synthesized frames and these exact training images is therefore partly a measure of training-set memorization, not an independent test of identity generalization. The paper's own anti-memorization argument in Appendix A.3 also relies on similarity to those same training images ('character similarity score' averaging over each CHARCHA image), so the claimed 'faithfully represented' result is not assessed against any external identity reference.

full rationale

The main music-to-video derivation is a pipeline of externally pretrained systems: Whisper for lyrics, a DEAM-trained network for valence/arousal, ChatGPT for prompt conditioning, Stable Diffusion for image generation, and spherical interpolation for video frames. No claimed first-principles result in that chain reduces to its own input. The only self-citation ([43], which includes an author of this paper) is invoked for the non-load-bearing point that style consistency depends on checkpoint models rather than prompt engineering. The significant circular element is localized to the personalization claim: the LoRA is fitted on CHARCHA images, and the same images are used as the evaluation reference for identity preservation, so the reported 92% face-match and CLIP similarities partly encode fit-to-training-data rather than an independent prediction. The CHARCHA security claim has a separate liveness-versus-authorization gap and an unreported experimental threshold of 6/10, but those are evidence and correctness issues, not circular derivations under the stated rubric; the paper's Limitations also concedes vulnerability to DeepFaceLive. Overall, the central music-video generation content has independent substance, so the circularity score is moderate rather than severe.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The pipeline's contribution is mostly compositional: it relies on Whisper, a DEAM-trained emotion network, GPT-4o, Stable Diffusion 1.5, Civitai checkpoints, MediaPipe, and DreamBooth LoRA as trusted building blocks. The tunable degrees of freedom that affect the claimed behavior are the CHARCHA threshold and timing, the 5-second emotion window with quadrant-crossing logic, and the fixed 7-image LoRA setup; none of these are supported by ablations or error bars. The security claim adds an ad hoc assumption that a webcam action check resists spoofing, which the paper itself flags as future work.

free parameters (3)
  • CHARCHA pass threshold and timing scheme = 6/10 threshold; 6 actions; 10 seconds each; 5-second gaps; 60-90 seconds total
    The paper states the 6/10 threshold is 'determined experimentally' but provides no experimental statistics; the timing values are hand-set design choices that the security property depends on.
  • Emotion change detection window = 5-second windows with running-sum quadrant crossing
    The arousal/valence model outputs a pair every 5 seconds and triggers a prompt change when a running sum crosses an emotion quadrant; the window length and running-sum rule are heuristics that determine where video prompts change.
  • LoRA training image count = 7 webcam images
    Personalization quality is assumed to work with a fixed set of 7 CHARCHA images; no ablation on image count or diversity is provided.
assumptions (4)
  • domain assumption Pretrained third-party models (Whisper, Stable Diffusion 1.5, GPT-4o, MediaPipe, openSMILE) behave as documented and introduce no material failure modes.
    The entire pipeline is built on these external tools (§3.1, §3.2); no ablations test model failures beyond the appendix's note that style prompting failed without checkpoints.
  • domain assumption The DEAM-trained emotion recognizer generalizes from its training set to arbitrary user tracks, genres, and languages, including Rick Astley, Miley Cyrus, and Adele examples used in the paper.
    Section §3.1 and appendix §A.4 apply the model zero-shot and report only a validation MSE of 0.0206, with no test set or cross-genre evaluation.
  • domain assumption A single LoRA trained on 7 images preserves identity well enough for the claimed personalization metrics.
    The face-verification results in §4.2 depend on this generalization; no study of image count, pose diversity, or lighting is given.
  • ad hoc to paper CHARCHA's action set and scoring cannot be bypassed by non-consenting parties using photos, videos, or deepfakes.
    This is the security premise of the protocol (§3.2), asserted with a tuned threshold; the paper's own §5 says defenses against DeepFaceLive are future work.

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Cite this review

Pith. "Pith review of Secure & Personalized Music-to-Video Generation via CHARCHA." pith.science (2026). https://pith.science/paper/IFGUAWUT

@misc{pith2026250202610,
  author       = {Pith},
  title        = {Pith review of: Secure & Personalized Music-to-Video Generation via CHARCHA},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IFGUAWUT}},
  note         = {Machine review of arXiv:2502.02610}
}
read the original abstract

Music is a deeply personal experience and our aim is to enhance this with a fully-automated pipeline for personalized music video generation. Our work allows listeners to not just be consumers but co-creators in the music video generation process by creating personalized, consistent and context-driven visuals based on lyrics, rhythm and emotion in the music. The pipeline combines multimodal translation and generation techniques and utilizes low-rank adaptation on listeners' images to create immersive music videos that reflect both the music and the individual. To ensure the ethical use of users' identity, we also introduce CHARCHA (patent pending), a facial identity verification protocol that protects people against unauthorized use of their face while at the same time collecting authorized images from users for personalizing their videos. This paper thus provides a secure and innovative framework for creating deeply personalized music videos.

Figures

Figures reproduced from arXiv: 2502.02610 by the authors.

Figure 1
Figure 1. Image stills and lyrics from generated music videos for Rick Astley’s "Never Gonna Give [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Image generation based on the lyric "I just wanna tell you how I’m feeling", progressively [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Left: valence/arousal emotion spectrum. Right: [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: 7 CHARCHA Protocol Actions & their backend detection using MediaPipe [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Survey of CHARCHA experiment with n=16 participants [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: MVP Model Architecture 10 [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Lyric and videoframe pairs using vanilla SD 1.5, ToonYou Beta 6 checkpoint [44], Realistic Vision V5.1 checkpoint [3] and Realistic Vision + trained character LoRA in vertical order A.3 Evaluation of CHARCHA Images and Generated Video Frames For the same song (“Never G…
Figure 8
Figure 8. Figure 8: A series of generated frames and corresponding prompts based on an excerpt of "Skyfall" [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: CLIP Similarity between CHARCHA images and generated video frames [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Arousal/Valence Prediction Model A.6 Ethical Considerations Our final pipeline on a few rare occasions generates explicit content, particularly nudity, often during quick transitions. This can be partially attributed to the presence of unsafe content in intermediate o…

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Reference graph

Works this paper leans on

53 extracted references · 39 canonical work pages

  1. [1]

    Queratogray sketch (eddiemauro-mix) - v1.0 | stable diffusion checkpoint | civitai,

    eddiemauro, “Queratogray sketch (eddiemauro-mix) - v1.0 | stable diffusion checkpoint | civitai,” 2023. [Online]. Available: https://civitai.com/models/80357/ queratogray-sketch-eddiemauro-mix

    work page 2023

  2. [2]

    Western animation diffusion - v1 | stable diffusion checkpoint | civitai,

    Lykkon, “Western animation diffusion - v1 | stable diffusion checkpoint | civitai,” 2023. [Online]. Available: https://civitai.com/models/86546/western-animation-diffusion

    work page 2023

  3. [3]

    Realistic vision v6.0 b1 - v6.0 b1 (vae): Stable diffusion checkpoint,

    SG_161222, “Realistic vision v6.0 b1 - v6.0 b1 (vae): Stable diffusion checkpoint,” Jan 2023. [Online]. Available: https://civitai.com/models/4201?modelVersionId=245598

    work page 2023

  4. [4]

    Ai music video generator,

    NeuralFrames, “Ai music video generator,” 2023, accessed: 2024-08-16. [Online]. Available: https://www.neuralframes.com/ai-music-video-generator

    work page 2023

  5. [5]

    Kaiber ai product,

    Kaiber AI, “Kaiber ai product,” 2023, accessed: 2024-08-16. [Online]. Available: https://kaiber.ai/product

    work page 2023

  6. [6]

    Sound2Sight: Generating Visual Dynamics from Sound and Context

    A. Cherian, M. Chatterjee, and N. Ahuja, “Sound2sight: Generating visual dynamics from sound and context,” 2020. [Online]. Available: https://arxiv.org/abs/2007.12130

    work page Pith review arXiv 2020

  7. [7]

    The power of sound (tpos): Audio reactive video generation with stable diffusion,

    Y . Jeong, W. Ryoo, S. Lee, D. Seo, W. Byeon, S. Kim, and J. Kim, “The power of sound (tpos): Audio reactive video generation with stable diffusion,” 2023. [Online]. Available: https://arxiv.org/abs/2309.04509

    arXiv 2023

  8. [8]

    MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

    L. Ruan, Y . Ma, H. Yang, H. He, B. Liu, J. Fu, N. J. Yuan, Q. Jin, and B. Guo, “Mm-diffusion: Learning multi-modal diffusion models for joint audio and video generation,” 2023. [Online]. Available: https://arxiv.org/abs/2212.09478

    work page Pith review arXiv 2023

Show all 53 references
  1. [9]

    Music mood detection based on audio and lyrics with deep neural net,

    R. Delbouys, R. Hennequin, F. Piccoli, J. Royo-Letelier, and M. Moussallam, “Music mood detection based on audio and lyrics with deep neural net,” 2018

    2018

  2. [10]

    Opensmile: The munich versatile and fast open-source audio feature extractor,

    F. Eyben, M. Wöllmer, and B. Schuller, “Opensmile: The munich versatile and fast open-source audio feature extractor,” in Proceedings of the 18th ACM International Conference on Multimedia, ser. MM ’10. New York, NY , USA: Association for Computing Machinery, 2010, p. 1459–146...

  3. [11]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” 2021. [Online]. Available: https: //arxiv.org/abs/2112.10752

    2021 arXiv

  4. [12]

    Photorealistic text-to-image diffusion models with deep language understanding,

    C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes, T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi, “Photorealistic text-to-image diffusion models with deep language understanding,” 2022. [Online]. Availabl...

    2022 arXiv

  5. [13]

    A very preliminary analysis of dall-e 2,

    G. Marcus, E. Davis, and S. Aaronson, “A very preliminary analysis of dall-e 2,” 2022. [Online]. Available: https://arxiv.org/abs/2204.13807 7

    2022 arXiv

  6. [14]

    Sdxl: Improving latent diffusion models for high-resolution image synthesis,

    D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rombach, “Sdxl: Improving latent diffusion models for high-resolution image synthesis,”

  7. [15]

    Github - black-forest-labs/flux: Official inference repo for flux.1 models,

    black-forest labs, “Github - black-forest-labs/flux: Official inference repo for flux.1 models,”

  8. [16]

    Available: https://pika.art/

    Pika, “Pika.” [Online]. Available: https://pika.art/

  9. [17]

    Luma dream machine

    L. AI, “Luma dream machine.” [Online]. Available: https://lumalabs.ai/dream-machine

  10. [18]

    Runway research | introducing gen-3 alpha: A new frontier for video generation,

    R. ML, “Runway research | introducing gen-3 alpha: A new frontier for video generation,”

  11. [19]

    Sora: A review on background, technology, limitations, and opportunities of large vision models,

    Y . Liu, K. Zhang, Y . Li, Z. Yan, C. Gao, R. Chen, Z. Yuan, Y . Huang, H. Sun, J. Gao, L. He, and L. Sun, “Sora: A review on background, technology, limitations, and opportunities of large vision models,” 2024. [Online]. Available: https://arxiv.org/abs/2402.17177

    2024 arXiv

  12. [20]

    Scalable diffusion models with transformers,

    W. Peebles and S. Xie, “Scalable diffusion models with transformers,” 2023. [Online]. Available: https://arxiv.org/abs/2212.09748

    2023 arXiv

  13. [21]

    Available: https://runwayml.com/research/introducing-gen-3-alpha

    [Online]. Available: https://runwayml.com/research/introducing-gen-3-alpha

  14. [22]

    Open-sora/docs/report_03.md at main · hpcaitech/open-sora,

    hpcaitech, “Open-sora/docs/report_03.md at main · hpcaitech/open-sora,” 2024. [Online]. Available: https://github.com/hpcaitech/Open-Sora/blob/main/docs/report_03.md

    2024

  15. [23]

    Captcha: using hard ai problems for security,

    L. V . Ahn, M. Blum, N. J. Hopper, and J. Langford, “Captcha: using hard ai problems for security,” in Proceedings of the 22nd International Conference on Theory and Applications of Cryptographic Techniques, ser. EUROCRYPT’03. Berlin, Heidelberg: Springer-Verlag, 2003, p. 294–311

    2003

  16. [24]

    Stable video diffusion: Scaling latent video diffusion models to large datasets,

    A. Blattmann, T. Dockhorn, S. Kulal, D. Mendelevitch, M. Kilian, D. Lorenz, Y . Levi, Z. English, V . V oleti, A. Letts, V . Jampani, and R. Rombach, “Stable video diffusion: Scaling latent video diffusion models to large datasets,” 2023. [Online]. Available: https://arxiv.org...

    2023 arXiv

  17. [25]

    Deep learning for deepfakes creation and detection: A survey,

    T. T. Nguyen, Q. V . H. Nguyen, D. T. Nguyen, D. T. Nguyen, T. Huynh-The, S. Nahavandi, T. T. Nguyen, Q.-V . Pham, and C. M. Nguyen, “Deep learning for deepfakes creation and detection: A survey,” Computer Vision and Image Understanding , vol. 223, p. 103525, Oct

  18. [26]

    celebrity stable diffusion ai models | civitai,

    civitai, “celebrity stable diffusion ai models | civitai,” 2024. [Online]. Available: https://civitai.com/tag/celebrity

    2024

  19. [27]

    An empirical study & evaluation of modern captchas,

    A. Searles, Y . Nakatsuka, E. Ozturk, A. Paverd, G. Tsudik, and A. Enkoji, “An empirical study & evaluation of modern captchas,” 2023. [Online]. Available: https://arxiv.org/abs/2307.12108

    2023 arXiv

  20. [28]

    Benchmarking music emotion recognition sys- tems,

    A. Alajanki, Y .-H. Yang, and M. Soleymani, “Benchmarking music emotion recognition sys- tems,” PLOS ONE, 2016, under review

    2016

  21. [29]

    Hello gpt-4o,

    OpenAI, “Hello gpt-4o,” 2024, accessed: 2024-08-16. [Online]. Available: https: //openai.com/index/hello-gpt-4o/

    2024

  22. [30]

    Make Your Art Move with Stable Diffusion Anima- tions — towardsdatascience.com,

    D. Nair, “Make Your Art Move with Stable Diffusion Anima- tions — towardsdatascience.com,” https://towardsdatascience.com/ make-your-art-move-with-stable-diffusion-animations-80de62eec633, [Accessed 02- May-2023]

    2023

  23. [31]

    Robust speech recognition via large-scale weak supervision,

    A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” 2022. [Online]. Available: https://arxiv.org/abs/2212.04356

    2022 arXiv

  24. [32]

    Bmaltais/kohya_ss

    Bmaltais, “Bmaltais/kohya_ss.” [Online]. Available: https://github.com/bmaltais/kohya_ss?tab= readme-ov-file 8

  25. [33]

    Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,

    N. Ruiz, Y . Li, V . Jampani, Y . Pritch, M. Rubinstein, and K. Aberman, “Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,” 2023

    2023

  26. [34]

    Z. He, B. Sun, F. Juefei-Xu, H. Ma, A. Ramchandani, V . Cheung, S. Shah, A. Kalia, N. Zhang, P. Zhang, R. Sumbaly, P. Vajda, A. Sinha, and M. Genai, Imagine yourself: Tuning-Free Personalized Image Generation . Meta AI, Aug

  27. [35]

    Available: https://civitai.com/

    [Online]. Available: https://civitai.com/

  28. [36]

    Lora: Low-rank adaptation of large language models,

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” 2021

    2021

  29. [37]

    Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,

    N. Ruiz, Y . Li, V . Jampani, Y . Pritch, M. Rubinstein, and K. Aberman, “Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,” 2022. [Online]. Available: https://arxiv.org/abs/2208.12242

    2022 arXiv

  30. [38]

    A brief introduction to opencv,

    I. Culjak, D. Abram, T. Pribanic, H. Dzapo, and M. Cifrek, “A brief introduction to opencv,” in 2012 Proceedings of the 35th International Convention MIPRO , 2012, pp. 1725–1730

    2012

  31. [39]

    [Online]. Available: https://scontent-sjc3-1.xx.fbcdn.net/v/t39.2365-6/455202613_ 905334511631908_8424078344857058009_n.pdf?_nc_cat=107&ccb=1-7&_nc_sid= 3c67a6&_nc_ohc=hvfJltR6G6gQ7kNvgHSKNuK&_nc_ht=scontent-sjc3-1.xx&oh=00_ AY Ac2szhaBbdcOGu1YRQPSLo6GC6ayMimiurCj-NLOGryA&oe=66C77D43

  32. [40]

    Mediapipe: A framework for building perception pipelines,

    C. Lugaresi, J. Tang, H. Nash, C. McClanahan, E. Uboweja, M. Hays, F. Zhang, C.-L. Chang, M. G. Yong, J. Lee, W.-T. Chang, W. Hua, M. Georg, and M. Grundmann, “Mediapipe: A framework for building perception pipelines,” 2019. [Online]. Available: https://arxiv.org/abs/1906.08172

    2019 arXiv

  33. [41]

    Lightface: A hybrid deep face recognition framework,

    ——, “Lightface: A hybrid deep face recognition framework,” in 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) . IEEE, 2020, pp. 23–27. [Online]. Available: https://ieeexplore.ieee.org/document/9259802

    2020

  34. [42]

    iperov/deepfacelive,

    iperov, “iperov/deepfacelive,” Nov 2022. [Online]. Available: https://github.com/iperov/ DeepFaceLive

    2022

  35. [43]

    Towards equitable representation in text-to-image synthesis models with the cross-cultural understanding benchmark (ccub) dataset,

    Z. Liu, Y . Shin, B.-C. Okogwu, Y . Yun, L. Coleman, P. Schaldenbrand, J. Kim, and J. Oh, “Towards equitable representation in text-to-image synthesis models with the cross-cultural understanding benchmark (ccub) dataset,” 2023. [Online]. Available: https://arxiv.org/abs/2301.12073

    2023 arXiv

  36. [44]

    Vggface2: A dataset for recognising faces across pose and age,

    Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “Vggface2: A dataset for recognising faces across pose and age,” 2018. [Online]. Available: https://arxiv.org/abs/1710.08092

    2018 arXiv

  37. [45]

    A benchmark of facial recognition pipelines and co-usability performances of modules,

    S. I. Serengil and A. Ozpinar, “A benchmark of facial recognition pipelines and co-usability performances of modules,” Bilisim Teknolojileri Dergisi , vol. 17, no. 2, pp. 95–107, 2024. [Online]. Available: https://dergipark.org.tr/en/pub/gazibtd/issue/84331/1399077

    2024

  38. [46]

    Surveying the landscape of image captioning evaluation: A comprehensive taxonomy and novel ensemble method,

    U. Berger, G. Stanovsky, O. Abend, and L. Frermann, “Surveying the landscape of image captioning evaluation: A comprehensive taxonomy and novel ensemble method,” 2024. [Online]. Available: https://arxiv.org/abs/2408.04909

    2024 arXiv

  39. [47]

    Extracting predominant local pulse information from music recordings,

    P. Grosche and M. Muller, “Extracting predominant local pulse information from music recordings,” Trans. Audio, Speech and Lang. Proc. , vol. 19, no. 6, p. 1688–1701, aug 2011. [Online]. Available: https://doi.org/10.1109/TASL.2010.2096216

    2011

  40. [48]

    Librosa based assessment tool for music information re- trieval systems,

    P. Raguraman, M. R., and M. Vijayan, “Librosa based assessment tool for music information re- trieval systems,” in2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2019, pp. 109–114. 9 A Appendix / supplemental material A.1 MVP Model Architecture ...

    2019

  41. [49]

    Toonyou - beta 6: Stable diffusion checkpoint,

    Bradcatt, “Toonyou - beta 6: Stable diffusion checkpoint,” Jun 2023. [Online]. Available: https://civitai.com/models/30240/toonyou

    2023

  42. [50]

    CLIPScore: A reference-free evaluation metric for image captioning,

    J. Hessel, A. Holtzman, M. Forbes, R. Le Bras, and Y . Choi, “CLIPScore: A reference-free evaluation metric for image captioning,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , M.-F. Moens, X. Huang, L. Specia, and S. W.-t. Yih, Ed...

    2021

  43. [2022]

    Available: http://dx.doi.org/10.1016/j.cviu.2022.103525

    [Online]. Available: http://dx.doi.org/10.1016/j.cviu.2022.103525

    2022

  44. [2023]

    Available: https://arxiv.org/abs/2307.01952

    [Online]. Available: https://arxiv.org/abs/2307.01952

    arXiv

  45. [2024]

    Available: https://github.com/black-forest-labs/flux

    [Online]. Available: https://github.com/black-forest-labs/flux

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.